【摘要】 图像作为一种典型信号,理论上可由一系列基本信号构成。为寻找一组可重构图像的基本信号,提出了基于特征的基图像提取和重构方法,使得可由任意图像集进行基图像提取并可由提取的基图像重构内容无关的任意图像。使用特征提取算法从训练集图像中分解出一系列基图像,阐述了基图像分解和提取的算法流程,通过将测试集图像投影到k个基图像构成的空间中得到投影系数,建立由投影系数和基图像重构原图像的方法和过程。实验结果表明,通过控制基图像数量k,图像的重构误差和质量可以达到较高要求,基图像的提取和重构的图像具备内容无关性,同时,该方法对于图像抽象特征的理解、深度神经网络应用等具有重要作用。更多还原

【Abstract】 As one kind of typical signals,an image can theoretically be composed of a series of basic signals.In order to find a set of basic signals to reconstruct images,a method for obtaining basis images based on feature extraction and reconstructing images from them is proposed.It makes possible to obtain the basis images from any set of images and to reconstruct images from the obtained ones because it is content-independent.The algorithm flow of extracting a series of basis images from the training set of images by feature extraction algorithm is described.The system of reconstructing the original image from the projection coefficient and basis images by projecting the set of test images into the space formed by the k basis images is developed.The experimental results show that,by controlling the number of basis images,the error and quality of reconstucted images can achieve higher requirement,and the method for basis images extraction and image reconstruction is content-independent.At the same time,this method plays an important role in the understanding of abstract features of images and the deep neural network.更多还原